Statistics

This book by Professor Chih-Ling Tsai and co-author Allan D. R. McQuarrie from North Dakota State University describes procedures for selecting a model from a large set of competing statistical models.

In partially linear single-index models, Professor Chih-Ling Tsai and co-authors Hua Liang and Xiang Liu from the University of Rochester and Runze Li from Pennsylvania State University obtain the semiparametrically efficient profile least-squares estimators of regression coefficients. The authors also employ the smoothly clipped absolute deviation penalty (SCAD) approach to simultaneously select variables and estimate regression coefficients. The study shows that the resulting SCAD estimators are consistent and possess the oracle property.

In this study, Professor Chih-Ling Tsai and co-authors Yiyun Zhang and Runze Li apply the nonconcave penalized likelihood approach to obtain variable selections as well as shrinkage estimators. This approach relies heavily on the choice of regularization parameter, which controls the model complexity.

In this study, Professors Chih-Ling Tsai and co-authors Ning Zhu from the Shanghai Advanced Institute of Finance and Ming-Chun Wang from National Chengchi University use a data set from market participants in the Taiwan Stock Exchange Capitalization Weighted Stock Index options markets to demonstrate a strong positive relationship between prior trading outcomes and subsequent risk taking. In particular, investors in this market take above-average risks in afternoon trading after morning gains.

In this paper, Professors Prasad Naik and Chih-Ling Tsai, with co-author Peide Shi from Nuclear Safety Solutions Ltd., examine the problem of jointly selecting the number of components and variables in finite mixture regression models.

In Markov-switching regression models, Professors Prasad Naik, Chih-Ling Tsai and co-author Aaron Smith from the UC Davis Department of Agricultural and Resource Economics use Kullback–Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously.

Inverse regression methods facilitate dimension-reduction analyses of high-dimensional data by extracting a small number of factors that are linear combinations of the original predictor variables. But the estimated factors may not lend themselves readily to interpretation consistent with prior information.

in this paper, Professors Prasad Naik and Chih-Ling Tsai derive a new model selection criterion for single-index models, AIC, by minimizing the expected Kullback-Leibler distances between the true and candidate models.

The pro-posed criterion selects not only relevant variables but also the smoothing parameter for an unknown link function. Thus, it is a general selection criterion that provides a unifies approach to model selection across both parametric and nonparametric functions. Monte Carlo studies demonstrate that AIC performs satisfactorily in most situations.

The partial least squares (PLS) approach first constructs new explanatory variables, known as factors (or components), which are linear combinations of available predictor variables. A small subset of these factors is then chosen and retained for prediction.

In data-rich marketing environments (e.g., direct marketing or new product design), managers face an ever-growing need to reduce the number of variables effectively. To accomplish this goal, Professors Prasad Naik and Chih-Ling Tsai and co-author Michael Hagerty introduce a new method called sliced inverse regression (SIR), which finds factors by taking into account the information contained in both the dependent and independent variables.

Commercial market research firms provide information on advertising variables of interest, such as brand awareness or gross rating points, that are likely to contain measurement errors. This unreliability of measured variables induces bias in the estimated parameters of dynamic models of advertising. Consequently, advertisers either under- or overspend on advertising to maintain a desired level of brand awareness.

(Davis, CA) — The UC Davis Part-Time MBA program offered in Sacramento and the San Francisco Bay Area is ranked among the top 9% in the U.S., according to U.S. News & World Report’s latest graduate business school rankings.

At No. 29, this is the fourth consecutive year the UC Davis Part-Time MBA program is among the top AACSB International-accredited part-time MBA programs surveyed. This year, there were 323 part-time MBA programs surveyed.

(Davis, CA) — The UC Davis Graduate School of Management’s Full-Time MBA program is ranked among the premier business schools in the nation for the 20th consecutive year, according to U.S. News & World Report’s latest graduate business school rankings released today.

U.S. News’ latest ranking places the Full-Time MBA program at No. 48, placing it among the top 10% of the 464 Association to Advance Collegiate Schools of Business International’s accredited full-time MBA programs surveyed.

(Davis, Calif.) – With a joint goal of speeding the transfer of new technologies from the laboratory to the commercial marketplace, the UC Davis Graduate School of Management, Lawrence Livermore National Laboratory and Sandia National Laboratories have announced a new partnership for researchers to develop their entrepreneurial skills.

What opportunities, decisions, events have shaped your professional life?

My career path has been a climb across a jungle gym rather than a tangent up a corporate ladder. As a child, I used to thumb through the three-inch JCPenney catalogue, picking out the professional women who I would grow to be. I wanted to rule the world from a corner office in a suit and heels. I wanted to shed my humble origins and become Corporate Barbie.

Agilent Technologies’ Electronic Measurement Group is a $3.6 billion business that over the past decade has seen a dramatic shift in its customer base from U.S., and Western European customers to predominantly Asia-based customers. Today, the majority of the division’s revenues are generated outside of the U.S., with an increasing concentration in China.